As today’s software development practices form their foundations on the cloud through cloud support services, digital applications are more interconnected than ever before. If your IT agency is a dedicated AWS partner (or partnered with any other leading provider, for that matter) this implies a relationship with an entity apart from your development team. This is all well and good, but have you thought about how these facets need to come together for leveraging business outcomes? A sustainable software development lifecycle is one such component, but understanding your customers, employees and various other stakeholders is a whole other area of untapped potential.
Once again, the mix between software and the cloud comes into play. While your applications reach the masses, data is fed from every interaction, and into your databases that are located in the back-end. This is where the potential lies; by understanding this data better, your business will be on its way to enhancing everything from customer journeys to better products. However, extracting insights from your data is a fairly complex undertaking – even for the simplest of data repositories. But this hasn’t deterred today’s businesses from venturing into data territory, for the potential that lies within is one that is bound to bring true value and competitiveness.
The many fields of studying data have long since been increasing in popularity, but many fundamental aspects are still misconceived by the typical business owner. Big data, data science and data analytics are three terms that are commonly used, but since their use cases overlap, this leaves many confused. Here, we outline the definitions of each, with the aim of individually understanding each component – and how it falls into the bigger picture pertaining to business data.
If you own a digital application/product which collects data with every interaction a user makes, you’ve got big data on your hands already. What really distinguishes big data from its otherwise ‘data’ counterpart is the fact that big data is too overwhelming to be processed by basic analytics tools. On top of that, increasing data entries may also render databases saturated – another clear sign of classic big data.
As data is gathered with even the smallest interactions, big data can gradually pile up, thereby creating vast amounts of raw and unstructured data repositories. Extracting insights from raw data of this calibre via rudimentary tools and expertise can only do so much; advanced competencies are required so that numbers can be crunched to precisely address the challenges your business may be facing.
Ultimately, big data is always a requisite for any form of data-related study; it all boils down to how effective your strategy is for extracting insights from such repositories.
When you have pools of big data that are already replete with figures, entries and everything else in between, you have two major ways to bring the value that all this data holds, to the forefront. Data science is one of them. By incorporating mathematical equations, programming expertise or a combination of the two, existing data can be processed to reveal trends and insights otherwise not observed by current business strategies.
One of the biggest advantages that data science offers to businesses is the ability to measure, change and repeat. An iterative process of this nature is an effective solution in the wake of aggressive business competition, especially since technology and consumer trends are always changing. On a more mainstream and general level, data science is the base for anything that is involved with machine learning, as identifying patterns from existing data sets is crucial for algorithms to learn and understand, for enhancing product outputs.
If your business has burning questions that have been left unanswered, digging deeper into your data to address the same can give you a sense of direction of what to do next, depending on what the numbers tell you. This is a prime example of data analytics, and is also one of the solutions you can adopt for making better sense of your big data. Modern Business Intelligence solutions focus on offering advanced data analytics capabilities, from intricate visualizations to customizations at a granular level.
On the whole, data analytics can also focus on obtaining data based on a question that businesses need a response to, with processing and reporting also following suit in the same way. This can be a highly targeted strategy to resolve chronic or persisting business problems – something which the industry of software development in Sri Lanka is also striving to do, through its data-centric digital services.
With cloud-based Business Intelligence involving easy setup with little to no IT intervention, data analytics has become more accessible. No matter what you specialize in and which department you hail from, access hosted Business Intelligence by filtering preferences to make your data work for you – and not the other way around.
Don’t forget about data integrity
As companies get busy in understanding loopholes and problems that can be fixed through data-backed decisions, identifying and securing the very source(s) of data commonly falls by the wayside. This can be detrimental for businesses, as poor data can reveal invalid insights, thereby leading to equally faulty business decisions.
Before you delve into data science or data analytics, ensure that your data is arriving from valid sources, and is well secured – with only authorized individuals being allowed to access it. Routine validation is also necessary, as even the smallest data items can lead to erroneous reporting.
With millions reliant on the modern digital landscape, vast amounts of data are being generated during any given moment. Within these data masses lies immense value, as records can reveal trends and outcomes that would’ve otherwise never caught the attention of anyone who is involved with your brand or business.
This is where big data, data science and data analytics come in. Understanding these three terms will give you the leverage you need to intelligently unveil insights that lie underneath otherwise raw data, for powerful decision making.